CN113190310B - Verification code design method based on random position object semantic recognition - Google Patents
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Abstract
The invention discloses a verification code generation and verification method based on random position object semantic recognition. The method comprises the following steps: s1), a verification code server receives a client verification code application; s2), the verification code server side selects any object group from the object database and randomly takes K object instances from the object group; the identifying code server randomly takes out 1 object example from other object groups of the object database; s3), the verification code server side generates a foreground picture and a verification code picture according to the following method; s4), the verification code server side enables the background picture, the foreground picture and the coordinate [ x ] P ,y P ]And sending the descriptive text to the client; s5), the verification code server receives a dragging track formed by dragging a mouse or sliding a finger by the client; s6), the verification code server side analyzes and judges the mouse or finger dragging behavior of the user, and feeds back the judgment result to the client side.
Description
The technical field is as follows:
the invention relates to a behavior verification code generation and verification method based on semantics, which is applied to the fields of computer application and information security.
Background art:
the verification code is a public full-automatic program for distinguishing whether a user is a human or a machine, can resist malicious password cracking and library collision of the machine through running a script program to simulate human behaviors, prevents harmful operations such as ticket swiping, wool pulling, malicious ordering, batch registration, malicious attack and the like, and protects information security and property security of websites and various users. The traditional identifying code comprises various forms such as pure letters, pure numbers, letter-number combination, arithmetic calculation and the like, and is easily identified automatically by a machine program. Then, the picture point-contact verification code appears, and verification is completed by manually recognizing the picture semantics and then clicking once or several times according to rules. However, with the development of machine vision technology, a simple picture point-touch verification code can still be quickly recognized by a machine program, if the number of picture point-touches is increased at a glance or the semantic recognition difficulty is increased, although the possibility of being recognized by the machine program can be reduced, the user is also troubled and unfriendly to use.
The behavior verification code is a verification code scheme depending on user behaviors, usually adopts dragging, connecting or other modes to replace keyboard input and mouse clicking, increases the difficulty of machine program simulation operation, and has the advantages of simple user operation and suitability for mobile phone screens.
The invention content is as follows:
aiming at the potential safety hazard existing in the prior identifying code technology, the invention provides a behavior identifying code design method based on random position object semantic identification.
In order to achieve the above purpose, the invention relates to a verification code generation and verification method based on random position object semantic recognition, which comprises the following steps:
s1), a verification code server receives a client verification code application;
s2), the verification code server side selects any object group from the object database, and randomly takes out K object examples from the object group; the identifying code server randomly takes out 1 object example from other object groups of the object database;
s3), the verification code server side generates a foreground picture and a verification code picture according to the following method;
31 Preparing a foreground picture with a distinct predetermined contour;
32 Adding a predetermined outline to the (K + 1) object examples obtained in each step two to form an object example picture;
33 Randomly superimposing the object example pictures on the background picture, wherein the outlines of the object example pictures are not shielded and not connected;
34 Record the coordinate x at which the geometric barycenter of the instance of the object of the same class is located 1 ,y 1 ],[x 2 ,y 2 ],…,[x K ,y K ]And the coordinate [ x ] of the geometric barycenter of the heterogeneous object instance K+1 ,y K+1 ]；
35 Synthesizing the background picture and the (K + 1) object instance pictures into a verification code picture;
36 The server randomly selects a point P in the range of the background picture for marking the position of foreground picture superposition; recording the coordinates [ x ] of the point P P ,y P ]；
S4), the verification code server side enables the background picture, the foreground picture and the coordinate [ x ] P ,y P ]And sending the descriptive text to the client;
s5), the verification code server receives a dragging track formed by dragging a mouse or sliding a finger by the client;
s6), the verification code server side analyzes and judges the mouse or finger dragging behavior of the user, and feeds back the judgment result to the client side.
Further, the analysis and decision process is as follows: the identifying code server judges whether the dragging terminal point is positioned in [ x ] K+1 ,y K+1 ]At least one of (1) and (b); the judgment method is to use the terminal point coordinate [ x, y]And [ x ] K+1 ,y K+1 ]Comparing the Euclidean distance with a preset judgment threshold T, if so, judging whether the Euclidean distance is larger than the preset judgment threshold T
The verification is passed, otherwise, the verification is not passed;
further, the predetermined contour may be a figure formed by any broken line or curve.
Further, the verification code server further judges the reasonability of the dragging track, if the track is reasonable, the verification is passed, otherwise, the verification is not passed.
Further, the rationality decision is: and calculating the actual length L of the track and the linear distance D from the dragging starting point to the dragging terminal point, setting alpha as a preset certain coefficient larger than 1, and judging that the track passes through if L is smaller than or equal to alpha D, otherwise, not passing through.
In order to achieve the purpose, the verification code generation method based on the random position object semantic recognition comprises the following steps:
61 Preparing a foreground picture with a distinct predetermined contour;
62 Prepare object instance picture:
selecting any object group from an object database, and randomly taking K object instances from the object group; then randomly taking out 1 object instance from other object groups of the object database;
adding a predetermined outline by using the obtained (K + 1) object instances to form an object instance picture;
63 Randomly superimposing the object example pictures on the background picture, wherein the outlines of the object example pictures are not shielded and not connected;
64 Record the coordinates [ x ] of the geometric barycenter of the same kind of object instance 1 ,y 1 ],[x 2 ,y 2 ],…,[x K ,y K ]And the coordinate [ x ] of the geometric barycenter of the heterogeneous object instance K+1 ,y K+1 ]；
65 Synthesize the background picture and (K + 1) object instance pictures into a verification code picture;
66 Randomly selecting a point P within the range of the background picture for marking the position of superposition of the foreground picture; recording the coordinates [ x ] of the point P P ,y P ]。
The verification code generation and verification method disclosed by the invention has the advantages that a plurality of similar objects and a heterogeneous object are displayed at the same time, the heterogeneous object is identified through semantic judgment at the client, the heterogeneous object is marked through a dragging behavior, and the verification process is completed according to the analysis and judgment of the dragging behavior. The invention effectively solves the problem that the current verification code is easy to be cracked by a machine program. The method inherits the advantages of simple operation and good experience of the dragging type verification code on one hand, inhibits the machine program from automatically recognizing the verification code through the disadvantage of the machine program on semantic recognition on the other hand, and can further increase the difficulty of the machine program for automatically recognizing the verification code by combining with the verification of the reasonability of a dragging track.
Drawings
FIG. 1 is a schematic diagram of a design example of an object database according to the present invention.
FIG. 2 is a schematic diagram of a verification code generation and verification process based on random location object semantic recognition.
Fig. 3 is a schematic diagram of the method for applying the verification code by the client.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
Example 1
Referring to the drawings, the specific technical implementation process of the behavior verification code design method based on the random position object semantic recognition of the embodiment is as follows:
the object database is managed at the captcha server as follows. (1) The database designs M groups of objects, wherein M is a positive integer, and each group of objects represents the same type of semantics. (2) Each group of objects holds not less than K object instances, K being a positive integer. I.e. for the mth group of objects (M =1,2, \ 8230;, M), there is N m An object instance, N m Is a positive integer and N m Is more than or equal to K. (3) the maintenance rules of the database include: a) Through manual discrimination, the semantics expressed by different classes of objects are ensured to have obvious difference; b) When any object instance is added into a database, the object instance is manually screened to ensure that the object instance highly conforms to the semantics of the object to which the object instance belongs, and no ambiguity exists; c) Any object instance appears only once in the database, i.e., only belongs to a certain class of objects. Fig. 1 gives an example of objects and object instances in a database, where K =8.
The generation and verification process of the verification code is shown in fig. 2, and includes:
step one, a verification code server receives a verification code application of a client.
And step two, the verification code server side selects any group of objects from the object database, and randomly takes K object instances (belonging to the same class of objects) from the objects. Then, the server randomly takes 1 object instance (belonging to a heterogeneous object) from other group objects in the object database.
Step three, the verification code server side generates a foreground picture and a verification code picture according to the following methods:
and preparing a foreground picture with a clear preset contour and transparent inside. The predetermined contour may be a figure made of any broken or curved line, preferably a square, rectangle or circle.
And (K + 1) object instances obtained in each step two are appropriately scaled and added with a preset outline to form an object instance picture. And then randomly overlaying the object instance pictures onto the background picture, wherein the object instance pictures meet the requirement that the outlines of the object instance pictures are not shielded and connected. The server records the coordinate [ x ] of the geometric gravity center of the similar object instance 1 ,y 1 ],[x 2 ,y 2 ],…,[x K ,y K ]And the coordinate [ x ] of the geometric barycenter of the heterogeneous object instance K+1 ,y K+1 ]. The background picture and the (K + 1) object instance pictures are synthesized into a verification code picture.
And the server randomly selects a point P in the range of the background picture for marking the position of the superposition of the foreground picture. Recording the coordinates [ x ] of the point P P ,y P ]。
Step four, the verification code server side enables the background picture, the foreground picture and the position coordinate [ x ] P ,y P ]And the descriptive text is sent to the client.
The verification code plug-in of the client side renders the received verification code picture and uses the coordinate [ x ] P ,y P ]The foreground picture is transparently displayed over the background picture layer for geometric center of gravity.
Fig. 3 shows an example of applying the present captcha method at the client.
The user interprets the semantics of (K + 1) object instances in the background map and identifies the distinctive object instance. And then, the user drags the foreground picture to the object instance freely by mouse dragging or finger sliding, so that the outlines of the foreground picture and the object instance are overlapped as much as possible.
And step five, receiving the dragging track (coordinates of each point dragged by the client, including the terminal point coordinates [ x, y ]) collected by the client through the verification code server.
And seventhly, analyzing and judging the dragging behavior of the mouse or the finger of the user by the verification code server side, and feeding back the judgment result to the client side. In the judgment process, the verification code server judges whether the dragging terminal point is positioned at [ x ] K+1 ,y K+1 ]The judgment method is to use the coordinates [ x, y]And [ x ] K+1 ,y K+1 ]Comparing the Euclidean distance with a preset judgment threshold T, if so, judging whether the Euclidean distance is larger than the preset judgment threshold T
The verification is passed, otherwise the verification is not passed.
Example 2
The behavior verification code design method based on random position object semantic recognition of the embodiment is implemented as follows:
the object database is managed at the captcha server as follows. (1) The database designs M groups of objects, wherein M is a positive integer, and each group of objects represents the same type of semantics. (2) Each group of objects holds not less than K object instances, K being a positive integer. I.e. for the mth group of objects (M =1,2, \8230;, M), there is N m An object instance, N m Is a positive integer and N m Is more than or equal to K. (3) the maintenance rules of the database include: a) Through manual discrimination, the semantics expressed by different classes of objects are ensured to have obvious difference; b) When any object instance is added into a database, manual screening is carried out to ensure that the object instance highly conforms to the semantics of the object to which the object instance belongs, and ambiguity does not exist; c) Any object instance appears only once in the database, i.e., only belongs to a certain class of objects. Fig. 1 gives an example of objects and object instances in a database, where K =8.
The generation and verification process of the verification code is shown in fig. 2, and includes:
step one, a verification code server receives a verification code application of a client.
And step two, the verification code server side selects any group of objects from the object database, and randomly takes K object instances (belonging to the same class of objects) from the objects. Then, the server randomly takes 1 object instance (belonging to a heterogeneous object) from other group objects in the object database.
Step three, the verification code server side generates a foreground picture and a verification code picture according to the following methods:
and preparing a foreground picture with a clear and preset outline and transparent inside. The predetermined contour may be a figure made of any broken or curved line, preferably a square, rectangle or circle.
And (K + 1) object instances obtained in each step two are appropriately scaled and added with a preset outline to form an object instance picture. And then randomly overlaying the object instance pictures onto the background picture, wherein the object instance pictures meet the requirement that the outlines of the object instance pictures are not shielded and connected. The server records the coordinate [ x ] of the geometric gravity center of the similar object instance 1 ,y 1 ],[x 2 ,y 2 ],…,[x K ,y K ]And the coordinate [ x ] of the geometric barycenter of the heterogeneous object instance K+1 ,y K+1 ]. The background picture and the (K + 1) object instance pictures are synthesized into a verification code picture.
And the server randomly selects a point P in the range of the background picture for marking the position of foreground picture superposition. Recording the coordinates [ x ] of the point P P ,y P ]。
The server also generates a string of explanatory words for providing necessary prompts for the user to carry out verification operation.
Step four, the verification code server side enables the background picture, the foreground picture and the position coordinate [ x ] P ,y P ]And the descriptive text is sent to the client.
The verification code plug-in of the client side renders the received verification code picture and uses the coordinate [ x ] P ,y P ]The foreground picture is transparently displayed over the background picture layer for geometric center of gravity.
The client also displays the explanatory text outside the background image in a proper manner or renders the explanatory text right-angled on the background image.
Fig. 3 shows an example of applying the present captcha method at the client.
The user interprets the semantics of (K + 1) object instances in the background map and identifies the distinctive one of the object instances. And then, the user drags the foreground picture to the object instance freely by mouse dragging or finger sliding, so that the outlines of the foreground picture and the object instance are overlapped as much as possible.
And step five, the verification code server receives the dragging track (coordinates of each point dragged by the client, including the terminal point coordinates [ x, y ]) collected by the client.
And seventhly, the verification code server analyzes and judges the dragging behavior of the mouse or the finger of the user, and feeds back the judgment result to the client. In the judgment process, the verification code server judges whether the dragging terminal point is positioned at [ x ] K+1 ,y K+1 ]The judgment method is to use the coordinates [ x, y]And [ x ] K+1 ,y K+1 ]Comparing the Euclidean distance with a preset judgment threshold T, if so, judging whether the Euclidean distance is larger than the preset judgment threshold T
The authentication is passed and otherwise the authentication is not passed.
The verification code server further judges the reasonability of the dragging track, if the track is reasonable, the verification is passed, otherwise, the verification is not passed. The rationality decision is: and calculating the actual length L of the track and the linear distance D from the dragging starting point to the dragging terminal point, setting alpha as a preset certain coefficient larger than 1, and judging that the track passes through if L is smaller than or equal to alpha D, otherwise, not passing through.
It should be apparent that the described embodiments are only some embodiments of the present invention, and not all embodiments. Moreover, the schematic diagrams provided in the embodiments are only for illustrating the basic concept of the present invention in a schematic manner, so as to be understood and read by those skilled in the art, and are not used to limit the practical limitations of the present invention. Various modifications based on the embodiments are within the scope of the present invention.
Claims (6)
1. A verification code generation and verification method based on random position object semantic recognition is characterized by comprising the following steps:
s1), a verification code server receives a client verification code application;
s2), the verification code server side selects any object group from the object database and randomly takes K object instances from the object group; the identifying code server randomly takes out 1 object example from other object groups of the object database;
s3), the verification code server generates a foreground picture and a verification code picture according to the following methods;
31 Preparing a foreground picture with a distinct predetermined contour;
32 Adding a predetermined contour to the (K + 1) object instances obtained in each step two to form an object instance picture;
33 Randomly superimposing the object example pictures on the background picture, wherein the outlines of the object example pictures are not shielded and not connected;
34 Record the coordinates [ x ] of the geometric barycenter of the same kind of object instance 1 ,y 1 ],[x 2 ,y 2 ],…,[x K ,y K ]And the coordinate [ x ] of the geometric barycenter of the heterogeneous object instance K+1 ,y K+1 ]；
35 Synthesize the background picture and (K + 1) object instance pictures into a verification code picture;
36 The server randomly selects a point P in the range of the background picture for marking the position of foreground picture superposition; recording the coordinates [ x ] of the point P P ,y P ]；
S4), the verification code server side enables the background picture, the foreground picture and the coordinate [ x ] P ,y P ]And sending the descriptive text to the client;
s5), the verification code server receives a dragging track formed by dragging a mouse or sliding a finger by the client;
s6), the verification code server analyzes and judges the mouse or finger dragging behavior of the user, and feeds back the judgment result to the client;
wherein,
m groups of objects are designed in the database, M is a positive integer, and each group of objects represents the same type of semantics; the maintenance rules of the database are as follows: a) The semantics expressed by different classes of objects have obvious difference; b) When any object instance is added into the database, the object instance conforms to the semantics of the object, and ambiguity does not exist; c) Any object instance appears only once in the database.
2. The method for generating and verifying verification code based on semantic recognition of random position object as claimed in claim 1, wherein said analyzing and deciding process is: the identifying code server judges whether the dragging terminal point is positioned in [ x ] K+1 ,y K+1 ]Treating; the judgment method is to use the terminal point coordinate [ x, y]And [ x ] K+1 ,y K+1 ]Comparing the Euclidean distance with a preset judgment threshold T, if so, judging whether the Euclidean distance is larger than the preset judgment threshold T
The verification is passed, otherwise the verification is not passed.
3. The method as claimed in claim 1, wherein the predetermined contour is a graph formed by any broken line or curve.
4. The verification code generation and verification method based on random position object semantic recognition according to claim 1, characterized in that the verification code server further judges the reasonability of the dragging track, if the track is reasonable, the verification is passed, otherwise, the verification is not passed.
5. The method for generating and verifying verification code based on semantic recognition of random position object according to claim 4, characterized in that the rationality decision is: and calculating the actual length L of the track and the linear distance D from the dragging starting point to the dragging terminal point, setting alpha as a preset certain coefficient larger than 1, and judging that the track passes through if L is smaller than or equal to alpha D, otherwise, not passing through.
6. A verification code generation method based on random position object semantic recognition is characterized by comprising the following steps:
61 Preparing a foreground picture with a distinct predetermined contour;
62 Prepare object instance picture:
selecting any object group from an object database, and randomly taking K object instances from the object group; then randomly taking out 1 object instance from other object groups of the object database;
adding a preset outline to the obtained (K + 1) object instances to form an object instance picture;
63 Randomly superimposing the object example pictures on the background picture, wherein the outlines of the object example pictures are not shielded and not connected;
64 Record the coordinates [ x ] of the geometric barycenter of the same kind of object instance 1 ,y 1 ],[x 2 ,y 2 ],…,[x K ,y K ]And the coordinate [ x ] of the geometric barycenter of the heterogeneous object instance K+1 ,y K+1 ]；
65 Synthesize the background picture and (K + 1) object instance pictures into a verification code picture;
66 Randomly selecting a point P in the range of the background picture for marking the position of the superposition of the foreground picture; recording the coordinates [ x ] of the point P P ,y P ]；
Wherein,
m groups of objects are designed in the database, M is a positive integer, and each group of objects represents the same type of semantics; the maintenance rules of the database are as follows: a) The semantics expressed by different classes of objects have obvious difference; b) When any object instance is added into the database, the object instance conforms to the semantics of the object, and ambiguity does not exist; c) Any object instance appears only once in the database.
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